Multiple hypothesis shape tracking using particle filtering and Hough-based observation models

29 January 2007 Multiple hypothesis shape tracking using particle filtering and Hough-based observation models

Alessio Dore, Majid Asadi, Carlo S. Regazzoni

Proceedings Volume 6508, Visual Communications and Image Processing 2007; 65080M (2007) https://doi.org/10.1117/12.714112
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

In the last years, the Particle Filter algorithm has been extensively proposed and employed for handling the problem of visual tracking of multiple moving objects under different assumptions. This wide usage is due to the capability of performing a recursive multiple hypothesis state estimation for non-linear non-Gaussian motion and observation models. In this paper a method, based on the Particle Filter framework, is proposed for multiple objects tracking, exploiting a target representation consisting of position and shape described as a fixed dimensionality vector composed by a fixed number of grouped target corners. However, usually, application domains of visual tracking algorithms are characterized by non-rigid objects and high occlusions rate entailing new corners to appear and others to disappear at each frame. In order to cope with this problem, a voting method (i.e. the Generalized Hough Transform) is employed to estimate the likelihood function to weight different propagated particles (i.e. multiple corners configurations describing shapes) by means of the corners extracted from the currently observed image. This method, in addition to the high dimensionality of the state representation, depicts the two main particularities of the presented Particle Filter. The proposed algorithm has been tested in a real-world domain and experiments indicate good results in tracking both rigid and non-rigid objects.

Citation Download Citation

Alessio Dore, Majid Asadi, and Carlo S. Regazzoni "Multiple hypothesis shape tracking using particle filtering and Hough-based observation models", Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65080M (29 January 2007); https://doi.org/10.1117/12.714112

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available